Using the NARMAX OLS-ERR algorithm to obtain the most influential parameters that affect the evolution of the magnetosphere

Details Using the NARMAX OLS-ERR algorithm to obtain the most influential parameters that affect the evolution of the magnetosphere Using the NARMAX OLS-ERR algorithm to obtain the most influential parameters that affect the evolution of the magnetosphere

Dec 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009agufmsm51a1327b&link_type=abstract

American Geophysical Union, Fall Meeting 2009, abstract #SM51A-1327

Physics

[2722] Magnetospheric Physics / Forecasting, [2784] Magnetospheric Physics / Solar Wind/Magnetosphere Interactions

Scientific paper

The magnetosphere is a complex nonlinear dynamical system that evolves under the solar wind's influence. In spite of numerous measurements it is still impossible to deduce from the first principals the ultimate mathematical model that can be used to predict the dynamics of the magnetosphere. A much simpler question which is not completely clear is: What are the best modelling inputs, the most influential parameters, that affect the evolution of the magnetosphere? Correlations between geomagnetic indices and solar wind parameters are often used to deduce these best input parameters. However it is obvious that this linear approach might lead to erroneous results for a nonlinear system such as the magnetosphere. This can be illustrated by the simple example of a quadratic stochastic system with a zero mean, white noise input X and output Y=X^2. Even though X is the best input parameter for such a system, the correlation between Y and X is zero for all delays and therefore implies that the parameter X does not affect the output Y. In this study a data based NARMAX OLS-ERR approach has been used to deduce best inputs to explain the dynamics of Dst based upon basic solar wind input parameters (Bx, By, Bs, Bn, Bz, V, Ti, n, P). For nonlinear systems the error reduction ratio (ERR) can be interpreted as a generalised indicator of correlation. The most influential parameter resulting from ERR was VBs. This is in a complete accordance with the best input deduced from basic principles in the Burton Model. The second most important parameter was identified as PBs. The importance of this parameter is not obvious from physical considerations. Other authors have had success in predicting the dynamics of the Dst index using the ɛ parameter ( ɛ = VBsin^4(θ/2)) as an input. This parameter has been compared, using the ERR, with the basic input parameters.

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